{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "77bf3edc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets,transforms\n",
    "import time\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06dde05d",
   "metadata": {},
   "source": [
    "# VGGNet的PyTorch复现（猫狗大战）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ff28d2",
   "metadata": {},
   "source": [
    "## 1. 数据集制作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2d0772",
   "metadata": {},
   "source": [
    "在论文中AlexNet作者使用的是ILSVRC 2012比赛数据集，该数据集非常大（有138G），下载、训练都很消耗时间，我们在复现的时候就不用这个数据集了。由于MNIST、CIFAR10、CIFAR100这些数据集图片尺寸都较小，不符合AlexNet网络输入尺寸227x227的要求，因此我们改用kaggle比赛经典的“猫狗大战”数据集了。\n",
    "\n",
    "该数据集包含的训练集总共25000张图片，猫狗各12500张，带标签；测试集总共12500张，不带标签。我们仅使用带标签的25000张图片，分别拿出2500张猫和狗的图片作为模型的验证集。我们按照以下目录层级结构，将数据集图片放好。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ffced5",
   "metadata": {},
   "source": [
    "![](./images/path.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3c97792",
   "metadata": {},
   "source": [
    "为了方便大家训练，我们将该数据集放在百度云盘，下载链接： 链接：https://pan.baidu.com/s/1UEOzxWWMLCUoLTxdWUkB4A 提取码：cdue"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d26f6e",
   "metadata": {},
   "source": [
    "### 1.1 制作图片数据的索引"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385ba135",
   "metadata": {},
   "source": [
    "准备好数据集之后，我们需要用PyTorch来读取并制作可以用来训练和测试的数据集。对于训练集和测试集，首先要分别制作对应的图片数据索引，即train.txt和test.txt两个文件，每个txt中包含每个图片的目录和对应类别class（cat对应的label=0，dog对应的label=1）。示意图如下："
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf021de9",
   "metadata": {},
   "source": [
    "![](./images/index.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fb70d5",
   "metadata": {},
   "source": [
    "制作图片数据索引train.txt和test.txt两个文件的python脚本程序如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d1a16003",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "train_txt_path = os.path.join(\"data\", \"catVSdog\", \"train.txt\")\n",
    "train_dir = os.path.join(\"data\", \"catVSdog\", \"train_data\")\n",
    "valid_txt_path = os.path.join(\"data\", \"catVSdog\", \"test.txt\")\n",
    "valid_dir = os.path.join(\"data\", \"catVSdog\", \"test_data\")\n",
    "\n",
    "def gen_txt(txt_path, img_dir):\n",
    "    f = open(txt_path, 'w')\n",
    "    \n",
    "    for root, s_dirs, _ in os.walk(img_dir, topdown=True):  # 获取 train文件下各文件夹名称\n",
    "        for sub_dir in s_dirs:\n",
    "            i_dir = os.path.join(root, sub_dir)             # 获取各类的文件夹 绝对路径\n",
    "            img_list = os.listdir(i_dir)                    # 获取类别文件夹下所有png图片的路径\n",
    "            for i in range(len(img_list)):\n",
    "                if not img_list[i].endswith('jpg'):         # 若不是png文件，跳过\n",
    "                    continue\n",
    "                #label = (img_list[i].split('.')[0] == 'cat')? 0 : 1 \n",
    "                label = img_list[i].split('.')[0]\n",
    "                # 将字符类别转为整型类型表示\n",
    "                if label == 'cat':\n",
    "                    label = '0'\n",
    "                else:\n",
    "                    label = '1'\n",
    "                img_path = os.path.join(i_dir, img_list[i])\n",
    "                line = img_path + ' ' + label + '\\n'\n",
    "                f.write(line)\n",
    "    f.close()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    gen_txt(train_txt_path, train_dir)\n",
    "    gen_txt(valid_txt_path, valid_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab24eea8",
   "metadata": {},
   "source": [
    "运行脚本之后就在./data/catVSdog/目录下生成train.txt和test.txt两个索引文件。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f0850e",
   "metadata": {},
   "source": [
    "### 1.2 构建Dataset子类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a8bd50e",
   "metadata": {},
   "source": [
    "PyTorch 加载自己的数据集，需要写一个继承自torch.utils.data中Dataset类，并修改其中的__init__方法、__getitem__方法、__len__方法。默认加载的都是图片，__init__的目的是得到一个包含数据和标签的list，每个元素能找到图片位置和其对应标签。然后用__getitem__方法得到每个元素的图像像素矩阵和标签，返回img和label。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f5c25c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "from torch.utils.data import Dataset\n",
    "\n",
    "class MyDataset(Dataset):\n",
    "    def __init__(self, txt_path, transform = None, target_transform = None):\n",
    "        fh = open(txt_path, 'r')\n",
    "        imgs = []\n",
    "        for line in fh:\n",
    "            line = line.rstrip()\n",
    "            words = line.split()\n",
    "            imgs.append((words[0], int(words[1]))) # 类别转为整型int\n",
    "            self.imgs = imgs \n",
    "            self.transform = transform\n",
    "            self.target_transform = target_transform\n",
    "    def __getitem__(self, index):\n",
    "        fn, label = self.imgs[index]\n",
    "        img = Image.open(fn).convert('RGB') \n",
    "        #img = Image.open(fn)\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img) \n",
    "        return img, label\n",
    "    def __len__(self):\n",
    "        return len(self.imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2a6a3c",
   "metadata": {},
   "source": [
    "getitem是核心函数。self.imgs是一个list，self.imgs[index]是一个str，包含图片路径，图片标签，这些信息是从上面生成的txt文件中读取；利用Image.open对图片进行读取，注意这里的img是单通道还是三通道的；self.transform(img)对图片进行处理，这个transform里边可以实现减均值、除标准差、随机裁剪、旋转、翻转、放射变换等操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c4ce64",
   "metadata": {},
   "source": [
    "### 1.3 加载数据集和数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbebcc0d",
   "metadata": {},
   "source": [
    "当Mydataset构建好，剩下的操作就交给DataLoder来加载数据集。在DataLoder中，会触发Mydataset中的getiterm函数读取一张图片的数据和标签，并拼接成一个batch返回，作为模型真正的输入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b347175",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipline_train = transforms.Compose([\n",
    "    #transforms.RandomResizedCrop(224),\n",
    "    transforms.RandomHorizontalFlip(),  #随机旋转图片\n",
    "    #将图片尺寸resize到224x224\n",
    "    transforms.Resize((224,224)),\n",
    "    #将图片转化为Tensor格式\n",
    "    transforms.ToTensor(),\n",
    "    #正则化(当模型出现过拟合的情况时，用来降低模型的复杂度)\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    #transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
    "])\n",
    "pipline_test = transforms.Compose([\n",
    "    #将图片尺寸resize到224x224\n",
    "    transforms.Resize((224,224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    #transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
    "])\n",
    "train_data = MyDataset('./data/catVSdog/train.txt', transform=pipline_train)\n",
    "test_data = MyDataset('./data/catVSdog/test.txt', transform=pipline_test)\n",
    "\n",
    "#train_data 和test_data包含多有的训练与测试数据，调用DataLoader批量加载\n",
    "trainloader = torch.utils.data.DataLoader(dataset=train_data, batch_size=64, shuffle=True)\n",
    "testloader = torch.utils.data.DataLoader(dataset=test_data, batch_size=32, shuffle=False)\n",
    "# 类别信息也是需要我们给定的\n",
    "classes = ('cat', 'dog') # 对应label=0，label=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ed2b13",
   "metadata": {},
   "source": [
    "在数据预处理中，我们将图片尺寸调整到224x224，符合VGGNet网络的输入要求。均值mean = [0.5, 0.5, 0.5]，方差std = [0.5, 0.5, 0.5]，然后使用transforms.Normalize进行归一化操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f5da6d",
   "metadata": {},
   "source": [
    "我们来看一下最终制作的数据集图片和它们对应的标签："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d4c30e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "examples = enumerate(trainloader)\n",
    "batch_idx, (example_data, example_label) = next(examples)\n",
    "# 批量展示图片\n",
    "for i in range(4):\n",
    "    plt.subplot(1, 4, i + 1)\n",
    "    plt.tight_layout()  #自动调整子图参数，使之填充整个图像区域\n",
    "    img = example_data[i]\n",
    "    img = img.numpy() # FloatTensor转为ndarray\n",
    "    img = np.transpose(img, (1,2,0)) # 把channel那一维放到最后\n",
    "    img = img * [0.5, 0.5, 0.5] + [0.5, 0.5, 0.5]\n",
    "    #img = img * [0.229, 0.224, 0.225] + [0.485, 0.456, 0.406]\n",
    "    plt.imshow(img)\n",
    "    plt.title(\"label:{}\".format(example_label[i]))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a262c6",
   "metadata": {},
   "source": [
    "## 搭建VGGNet神经网络结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cbfa4901",
   "metadata": {},
   "outputs": [],
   "source": [
    "class VGG(nn.Module):\n",
    "    def __init__(self, features, num_classes=2, init_weights=False):\n",
    "        super(VGG, self).__init__()\n",
    "        self.features = features\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(512*7*7, 500),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(500, 20),\n",
    "            nn.ReLU(True),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(20, num_classes)\n",
    "        )\n",
    "        if init_weights:\n",
    "            self._initialize_weights()\n",
    "\n",
    "    def forward(self, x):\n",
    "        # N x 3 x 224 x 224\n",
    "        x = self.features(x)\n",
    "        # N x 512 x 7 x 7\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        # N x 512*7*7\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n",
    "                nn.init.xavier_uniform_(m.weight)\n",
    "                if m.bias is not None:\n",
    "                    nn.init.constant_(m.bias, 0)\n",
    "            elif isinstance(m, nn.Linear):\n",
    "                nn.init.xavier_uniform_(m.weight)\n",
    "                # nn.init.normal_(m.weight, 0, 0.01)\n",
    "                nn.init.constant_(m.bias, 0)\n",
    "\n",
    "\n",
    "def make_features(cfg: list):\n",
    "    layers = []\n",
    "    in_channels = 3\n",
    "    for v in cfg:\n",
    "        if v == \"M\":\n",
    "            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n",
    "        else:\n",
    "            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n",
    "            layers += [conv2d, nn.ReLU(True)]\n",
    "            in_channels = v\n",
    "    return nn.Sequential(*layers)\n",
    "\n",
    "\n",
    "cfgs = {\n",
    "    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n",
    "    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n",
    "    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],\n",
    "    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],\n",
    "}\n",
    "\n",
    "\n",
    "def vgg(model_name=\"vgg16\", **kwargs):\n",
    "    assert model_name in cfgs, \"Warning: model number {} not in cfgs dict!\".format(model_name)\n",
    "    cfg = cfgs[model_name]\n",
    "\n",
    "    model = VGG(make_features(cfg), **kwargs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45afba5d",
   "metadata": {},
   "source": [
    "首先，我们从VGG 6个结构中选择了A、B、D、E这四个来搭建模型，建立的cfg字典包含了这4个结构。例如对于vgg16，`[64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']`表示了卷积层的结构。64表示conv3-64，'M'表示maxpool，128表示conv3-128，256表示conv3-256，512表示conv3-512。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ceb651",
   "metadata": {},
   "source": [
    "![](./images/vgg.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb99499",
   "metadata": {},
   "source": [
    "选定好哪个VGG结构之后，将该列表传入到函数make_features()中，构建VGG的卷积层，函数返回实例化模型。例如我们来构建vgg16的卷积层结构并打印看看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7702c9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (1): ReLU(inplace=True)\n",
       "  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (3): ReLU(inplace=True)\n",
       "  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (6): ReLU(inplace=True)\n",
       "  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (8): ReLU(inplace=True)\n",
       "  (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (11): ReLU(inplace=True)\n",
       "  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (13): ReLU(inplace=True)\n",
       "  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (15): ReLU(inplace=True)\n",
       "  (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (18): ReLU(inplace=True)\n",
       "  (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (20): ReLU(inplace=True)\n",
       "  (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (22): ReLU(inplace=True)\n",
       "  (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (25): ReLU(inplace=True)\n",
       "  (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (27): ReLU(inplace=True)\n",
       "  (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (29): ReLU(inplace=True)\n",
       "  (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       ")"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfg = cfgs['vgg16']\n",
    "make_features(cfg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e4a2f5",
   "metadata": {},
   "source": [
    "定义VGG类的时候，参数num_classes指的是类别的数量，由于我们这里的数据集只有猫和狗两个类别，因此这里的全连接层的神经元个数做了微调。num_classes=2，输出层也是两个神经元，不是原来的1000个神经元。FC4096由原来的4096个神经元分别改为500、20个神经元。这里的改动大家注意一下，根据实际数据集的类别数量进行调整。整个网络的其它结构跟论文中的完全一样。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445f5925",
   "metadata": {},
   "source": [
    "函数initialize_weights()是对网络参数进行初始化操作，这里我们默认选择关闭初始化操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6414ae8f",
   "metadata": {},
   "source": [
    "函数forward()定义了VGG网络的完整结构，这里注意最后的卷积层输出的featureMap是N x 512 x 7 x 7，N表示batchsize，需要将其展开为一维向量，方便与全连接层连接。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "952c8957",
   "metadata": {},
   "source": [
    "## 3. 将定义好的网络结构搭载到GPU/CPU，并定义优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b0c8762d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建模型，部署gpu\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_name = \"vgg16\"\n",
    "model = vgg(model_name=model_name, num_classes=2, init_weights=True)\n",
    "model.to(device)\n",
    "#定义优化器\n",
    "loss_function = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.0001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdacb778",
   "metadata": {},
   "source": [
    "## 4. 定义训练过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ac8b2b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_runner(model, device, trainloader, loss_function, optimizer, epoch):\n",
    "    #训练模型, 启用 BatchNormalization 和 Dropout, 将BatchNormalization和Dropout置为True\n",
    "    model.train()\n",
    "    total = 0\n",
    "    correct =0.0\n",
    "    \n",
    "    #enumerate迭代已加载的数据集,同时获取数据和数据下标\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data\n",
    "        #把模型部署到device上\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        #初始化梯度\n",
    "        optimizer.zero_grad()\n",
    "        #保存训练结果\n",
    "        outputs = model(inputs)\n",
    "        #计算损失和\n",
    "        #loss = F.cross_entropy(outputs, labels)\n",
    "        loss = loss_function(outputs, labels)\n",
    "        #获取最大概率的预测结果\n",
    "        #dim=1表示返回每一行的最大值对应的列下标\n",
    "        predict = outputs.argmax(dim=1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predict == labels).sum().item()\n",
    "        #反向传播\n",
    "        loss.backward()\n",
    "        #更新参数\n",
    "        optimizer.step()\n",
    "        if i % 100 == 0:\n",
    "            #loss.item()表示当前loss的数值\n",
    "            print(\"Train Epoch{} \\t Loss: {:.6f}, accuracy: {:.6f}%\".format(epoch, loss.item(), 100*(correct/total)))\n",
    "            Loss.append(loss.item())\n",
    "            Accuracy.append(correct/total)\n",
    "    return loss.item(), correct/total"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87859425",
   "metadata": {},
   "source": [
    "## 5. 定义测试过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dda5fc20",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_runner(model, device, testloader):\n",
    "    #模型验证, 必须要写, 否则只要有输入数据, 即使不训练, 它也会改变权值\n",
    "    #因为调用eval()将不启用 BatchNormalization 和 Dropout, BatchNormalization和Dropout置为False\n",
    "    model.eval()\n",
    "    #统计模型正确率, 设置初始值\n",
    "    correct = 0.0\n",
    "    test_loss = 0.0\n",
    "    total = 0\n",
    "    #torch.no_grad将不会计算梯度, 也不会进行反向传播\n",
    "    with torch.no_grad():\n",
    "        for data, label in testloader:\n",
    "            data, label = data.to(device), label.to(device)\n",
    "            output = model(data)\n",
    "            test_loss += F.cross_entropy(output, label).item()\n",
    "            predict = output.argmax(dim=1)\n",
    "            #计算正确数量\n",
    "            total += label.size(0)\n",
    "            correct += (predict == label).sum().item()\n",
    "        #计算损失值\n",
    "        print(\"test_avarage_loss: {:.6f}, accuracy: {:.6f}%\".format(test_loss/total, 100*(correct/total)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb59c9da",
   "metadata": {},
   "source": [
    "## 6. 运行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb845c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start_time 2022-02-11 16:01:00\n",
      "Train Epoch1 \t Loss: 0.692880, accuracy: 54.687500%\n",
      "Train Epoch1 \t Loss: 0.693360, accuracy: 51.129332%\n",
      "Train Epoch1 \t Loss: 0.692834, accuracy: 50.412002%\n",
      "Train Epoch1 \t Loss: 0.693534, accuracy: 50.394518%\n",
      "test_avarage_loss: 0.021764, accuracy: 50.000000%\n",
      "end_time:  2022-02-11 16:04:28 \n",
      "\n",
      "start_time 2022-02-11 16:04:28\n",
      "Train Epoch2 \t Loss: 0.691072, accuracy: 62.500000%\n",
      "Train Epoch2 \t Loss: 0.693356, accuracy: 49.489480%\n",
      "Train Epoch2 \t Loss: 0.693212, accuracy: 49.790112%\n",
      "Train Epoch2 \t Loss: 0.692290, accuracy: 49.916944%\n",
      "test_avarage_loss: 0.021765, accuracy: 50.000000%\n",
      "end_time:  2022-02-11 16:07:56 \n",
      "\n",
      "start_time 2022-02-11 16:07:56\n",
      "Train Epoch3 \t Loss: 0.692666, accuracy: 48.437500%\n",
      "Train Epoch3 \t Loss: 0.692639, accuracy: 50.293936%\n",
      "Train Epoch3 \t Loss: 0.691348, accuracy: 50.396455%\n",
      "Train Epoch3 \t Loss: 0.689182, accuracy: 50.913621%\n",
      "test_avarage_loss: 0.021766, accuracy: 50.000000%\n",
      "end_time:  2022-02-11 16:11:23 \n",
      "\n",
      "start_time 2022-02-11 16:11:23\n",
      "Train Epoch4 \t Loss: 0.694357, accuracy: 40.625000%\n",
      "Train Epoch4 \t Loss: 0.695722, accuracy: 49.164604%\n",
      "Train Epoch4 \t Loss: 0.692976, accuracy: 49.681281%\n",
      "Train Epoch4 \t Loss: 0.692659, accuracy: 49.932517%\n",
      "test_avarage_loss: 0.021764, accuracy: 50.000000%\n",
      "end_time:  2022-02-11 16:14:50 \n",
      "\n",
      "start_time 2022-02-11 16:14:50\n",
      "Train Epoch5 \t Loss: 0.694126, accuracy: 37.500000%\n",
      "Train Epoch5 \t Loss: 0.693663, accuracy: 49.860767%\n",
      "Train Epoch5 \t Loss: 0.696319, accuracy: 50.015547%\n",
      "Train Epoch5 \t Loss: 0.693091, accuracy: 49.979236%\n",
      "test_avarage_loss: 0.021704, accuracy: 52.600000%\n",
      "end_time:  2022-02-11 16:18:17 \n",
      "\n",
      "start_time 2022-02-11 16:18:17\n",
      "Train Epoch6 \t Loss: 0.691204, accuracy: 48.437500%\n",
      "Train Epoch6 \t Loss: 0.653770, accuracy: 52.382426%\n",
      "Train Epoch6 \t Loss: 0.663840, accuracy: 54.112251%\n",
      "Train Epoch6 \t Loss: 0.629374, accuracy: 55.694560%\n",
      "test_avarage_loss: 0.020634, accuracy: 59.720000%\n",
      "end_time:  2022-02-11 16:21:43 \n",
      "\n",
      "start_time 2022-02-11 16:21:43\n",
      "Train Epoch7 \t Loss: 0.692236, accuracy: 42.187500%\n",
      "Train Epoch7 \t Loss: 0.600229, accuracy: 62.004950%\n",
      "Train Epoch7 \t Loss: 0.524689, accuracy: 63.292910%\n",
      "Train Epoch7 \t Loss: 0.600918, accuracy: 63.740656%\n",
      "test_avarage_loss: 0.017988, accuracy: 68.860000%\n",
      "end_time:  2022-02-11 16:25:09 \n",
      "\n",
      "start_time 2022-02-11 16:25:09\n",
      "Train Epoch8 \t Loss: 0.601056, accuracy: 73.437500%\n",
      "Train Epoch8 \t Loss: 0.483162, accuracy: 70.389851%\n",
      "Train Epoch8 \t Loss: 0.439118, accuracy: 71.299751%\n",
      "Train Epoch8 \t Loss: 0.519363, accuracy: 72.482350%\n",
      "test_avarage_loss: 0.014317, accuracy: 78.700000%\n",
      "end_time:  2022-02-11 16:28:35 \n",
      "\n",
      "start_time 2022-02-11 16:28:35\n",
      "Train Epoch9 \t Loss: 0.532053, accuracy: 70.312500%\n",
      "Train Epoch9 \t Loss: 0.316171, accuracy: 79.687500%\n",
      "Train Epoch9 \t Loss: 0.453879, accuracy: 80.503731%\n",
      "Train Epoch9 \t Loss: 0.428646, accuracy: 81.016404%\n",
      "test_avarage_loss: 0.010464, accuracy: 86.100000%\n",
      "end_time:  2022-02-11 16:32:01 \n",
      "\n",
      "start_time 2022-02-11 16:32:01\n",
      "Train Epoch10 \t Loss: 0.319294, accuracy: 90.625000%\n",
      "Train Epoch10 \t Loss: 0.241084, accuracy: 84.653465%\n",
      "Train Epoch10 \t Loss: 0.383309, accuracy: 85.253420%\n",
      "Train Epoch10 \t Loss: 0.331237, accuracy: 85.781769%\n",
      "test_avarage_loss: 0.010724, accuracy: 84.480000%\n",
      "end_time:  2022-02-11 16:35:29 \n",
      "\n",
      "start_time 2022-02-11 16:35:29\n",
      "Train Epoch11 \t Loss: 0.401290, accuracy: 84.375000%\n",
      "Train Epoch11 \t Loss: 0.315276, accuracy: 87.793936%\n",
      "Train Epoch11 \t Loss: 0.401207, accuracy: 87.966418%\n",
      "Train Epoch11 \t Loss: 0.349950, accuracy: 88.263081%\n",
      "test_avarage_loss: 0.008335, accuracy: 88.720000%\n",
      "end_time:  2022-02-11 16:38:56 \n",
      "\n",
      "start_time 2022-02-11 16:38:56\n",
      "Train Epoch12 \t Loss: 0.218023, accuracy: 90.625000%\n",
      "Train Epoch12 \t Loss: 0.274801, accuracy: 90.578589%\n",
      "Train Epoch12 \t Loss: 0.228344, accuracy: 90.003109%\n",
      "Train Epoch12 \t Loss: 0.149454, accuracy: 90.287583%\n",
      "test_avarage_loss: 0.008101, accuracy: 89.560000%\n",
      "end_time:  2022-02-11 16:42:22 \n",
      "\n",
      "start_time 2022-02-11 16:42:22\n",
      "Train Epoch13 \t Loss: 0.210163, accuracy: 93.750000%\n",
      "Train Epoch13 \t Loss: 0.268975, accuracy: 90.872525%\n",
      "Train Epoch13 \t Loss: 0.190048, accuracy: 91.153607%\n",
      "Train Epoch13 \t Loss: 0.238544, accuracy: 91.242733%\n",
      "test_avarage_loss: 0.007183, accuracy: 90.640000%\n",
      "end_time:  2022-02-11 16:45:48 \n",
      "\n",
      "start_time 2022-02-11 16:45:48\n",
      "Train Epoch14 \t Loss: 0.240310, accuracy: 92.187500%\n",
      "Train Epoch14 \t Loss: 0.127962, accuracy: 92.465965%\n",
      "Train Epoch14 \t Loss: 0.314684, accuracy: 92.545087%\n",
      "Train Epoch14 \t Loss: 0.111913, accuracy: 92.800042%\n",
      "test_avarage_loss: 0.005812, accuracy: 92.340000%\n",
      "end_time:  2022-02-11 16:49:14 \n",
      "\n",
      "start_time 2022-02-11 16:49:14\n",
      "Train Epoch15 \t Loss: 0.186681, accuracy: 92.187500%\n",
      "Train Epoch15 \t Loss: 0.133215, accuracy: 93.750000%\n",
      "Train Epoch15 \t Loss: 0.131675, accuracy: 93.477923%\n",
      "Train Epoch15 \t Loss: 0.159397, accuracy: 93.599460%\n",
      "test_avarage_loss: 0.005500, accuracy: 92.800000%\n",
      "end_time:  2022-02-11 16:52:42 \n",
      "\n",
      "start_time 2022-02-11 16:52:42\n",
      "Train Epoch16 \t Loss: 0.172398, accuracy: 93.750000%\n",
      "Train Epoch16 \t Loss: 0.057886, accuracy: 93.641708%\n",
      "Train Epoch16 \t Loss: 0.066633, accuracy: 94.231965%\n",
      "Train Epoch16 \t Loss: 0.192610, accuracy: 94.352159%\n",
      "test_avarage_loss: 0.004883, accuracy: 93.540000%\n",
      "end_time:  2022-02-11 16:56:09 \n",
      "\n",
      "start_time 2022-02-11 16:56:09\n",
      "Train Epoch17 \t Loss: 0.127656, accuracy: 93.750000%\n",
      "Train Epoch17 \t Loss: 0.064953, accuracy: 94.894802%\n",
      "Train Epoch17 \t Loss: 0.167811, accuracy: 94.636194%\n",
      "Train Epoch17 \t Loss: 0.170746, accuracy: 94.689576%\n",
      "test_avarage_loss: 0.004565, accuracy: 93.880000%\n",
      "end_time:  2022-02-11 16:59:34 \n",
      "\n",
      "start_time 2022-02-11 16:59:34\n",
      "Train Epoch18 \t Loss: 0.110685, accuracy: 95.312500%\n",
      "Train Epoch18 \t Loss: 0.155694, accuracy: 95.126856%\n",
      "Train Epoch18 \t Loss: 0.083463, accuracy: 95.304726%\n",
      "Train Epoch18 \t Loss: 0.140581, accuracy: 95.229444%\n",
      "test_avarage_loss: 0.005044, accuracy: 93.720000%\n",
      "end_time:  2022-02-11 17:03:01 \n",
      "\n",
      "start_time 2022-02-11 17:03:01\n",
      "Train Epoch19 \t Loss: 0.057113, accuracy: 96.875000%\n",
      "Train Epoch19 \t Loss: 0.203229, accuracy: 95.652847%\n",
      "Train Epoch19 \t Loss: 0.158384, accuracy: 95.786692%\n",
      "Train Epoch19 \t Loss: 0.119906, accuracy: 95.701827%\n",
      "test_avarage_loss: 0.004526, accuracy: 94.320000%\n",
      "end_time:  2022-02-11 17:06:27 \n",
      "\n",
      "start_time 2022-02-11 17:06:27\n",
      "Train Epoch20 \t Loss: 0.174919, accuracy: 90.625000%\n",
      "Train Epoch20 \t Loss: 0.038814, accuracy: 96.550124%\n",
      "Train Epoch20 \t Loss: 0.167036, accuracy: 96.330846%\n",
      "Train Epoch20 \t Loss: 0.053163, accuracy: 96.387043%\n",
      "test_avarage_loss: 0.004447, accuracy: 94.680000%\n",
      "end_time:  2022-02-11 17:09:53 \n",
      "\n",
      "Finished Training\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#调用\n",
    "epoch = 20\n",
    "Loss = []\n",
    "Accuracy = []\n",
    "for epoch in range(1, epoch+1):\n",
    "    print(\"start_time\",time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())))\n",
    "    loss, acc = train_runner(model, device, trainloader, loss_function, optimizer, epoch)\n",
    "    Loss.append(loss)\n",
    "    Accuracy.append(acc)\n",
    "    test_runner(model, device, testloader)\n",
    "    print(\"end_time: \",time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time())),'\\n')\n",
    "\n",
    "print('Finished Training')\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(Loss)\n",
    "plt.title('Loss')\n",
    "plt.show()\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(Accuracy)\n",
    "plt.title('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46dd536d",
   "metadata": {},
   "source": [
    "## 7. 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "79818db5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGG(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace=True)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace=True)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace=True)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace=True)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace=True)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace=True)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace=True)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace=True)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace=True)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace=True)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=25088, out_features=500, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Dropout(p=0.5, inplace=False)\n",
      "    (3): Linear(in_features=500, out_features=20, bias=True)\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): Dropout(p=0.5, inplace=False)\n",
      "    (6): Linear(in_features=20, out_features=2, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jysp/anaconda3/lib/python3.7/site-packages/torch/serialization.py:256: UserWarning: Couldn't retrieve source code for container of type VGG. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "print(model)\n",
    "torch.save(model, './models/vgg-catvsdog.pth') #保存模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fe1ec8f",
   "metadata": {},
   "source": [
    "## 8. 模型测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e44d90",
   "metadata": {},
   "source": [
    "下面使用一张猫狗大战测试集的图片进行模型的测试。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "921ccf2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "概率： tensor([[7.6922e-08, 1.0000e+00]], device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
      "预测类别： dog\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    model = torch.load('./models/vgg-catvsdog.pth') #加载模型\n",
    "    model = model.to(device)\n",
    "    model.eval()    #把模型转为test模式\n",
    "    \n",
    "    #读取要预测的图片\n",
    "    # 读取要预测的图片\n",
    "    img = Image.open(\"./images/test_dog.jpg\") # 读取图像\n",
    "    #img.show()\n",
    "    plt.imshow(img) # 显示图片\n",
    "    plt.axis('off') # 不显示坐标轴\n",
    "    plt.show()\n",
    "    \n",
    "    # 导入图片，图片扩展后为[1，1，32，32]\n",
    "    trans = transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize((227,227)),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "            #transforms.Normalize(mean = [0.485, 0.456, 0.406],std = [0.229, 0.224, 0.225])\n",
    "        ])\n",
    "    img = trans(img)\n",
    "    img = img.to(device)\n",
    "    img = img.unsqueeze(0)  #图片扩展多一维,因为输入到保存的模型中是4维的[batch_size,通道,长，宽]，而普通图片只有三维，[通道,长，宽]\n",
    "    \n",
    "    # 预测 \n",
    "    # 预测 \n",
    "    classes = ('cat', 'dog')\n",
    "    output = model(img)\n",
    "    prob = F.softmax(output,dim=1) #prob是2个分类的概率\n",
    "    print(\"概率：\",prob)\n",
    "    value, predicted = torch.max(output.data, 1)\n",
    "    predict = output.argmax(dim=1)\n",
    "    pred_class = classes[predicted.item()]\n",
    "    print(\"预测类别：\",pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8beef23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-pytorch] *",
   "language": "python",
   "name": "conda-env-.conda-pytorch-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
